What AI tool should we be using? The honest answer is that it is not as simple as recommending a car. Do you need a pickup truck, an SUV, or a commuter car? The right answer depends entirely on what you are trying to do. And the same is true for AI. Starting with the tool is backwards. If you start with a tool, you end up trying to find problems the tool can solve instead of solving the problems that matter.
I was in a conversation recently about one of our vendors. We were helping them think through a new tool. And I realized if we were not careful, we were going to end up designing a prettier, faster, cleaner version of what we already had, and paying for another platform to do the same thing. That is not progress. That is distraction.
The better approach is to start with the outcome. What are you trying to improve, simplify, speed up, or make more effective? Once you know that, then you can work backwards to figure out what the technology needs to do. And then you pick the tool.
Think of It Like Hiring
The clearest way I know to think about this is to treat it like a new hire.
When someone joins your team on their first day, you have already answered a lot of questions. What do they need access to? What systems do they use? What is their job description? What does a day in their role look like? You have a desired outcome for that person or you would not have hired them.
AI works the same way. What instructions does it need? What information does it need access to? Does it need examples, documents, or access to a specific system? And critically: what should it not be able to touch? Can it edit, delete, or create things? Or should it only be able to read?
Answering those questions before you choose a tool keeps the technology tied to the business outcome. And only after you have answered them should you ask which specific tool makes sense for the job.
Just because a platform has more features and more power does not mean it is the best fit. A backhoe is bigger and faster than a hand trowel. But if you are planting a single rose bush, the hand trowel is the right tool. The job determines the tool. Not the other way around.
Define the Type of Work First
Once you know the outcome, define the type of work. These categories are different from each other, and a tool that is great at one may not be the right choice for another.
Communication and research are two practical areas where AI creates real value. It can draft or improve emails, summarize information, help with proposals, donor communication, client messaging, and research. But be careful about what you feed into it. Do not take an entire email chain and drop it into a public AI tool. There can be sensitive information in that thread that has no business being processed externally. Better to put in only what you need and work from there.
The framing I find useful here: AI is not replacing the person doing the work. It is helping remove the time spent getting started. A task started is already half done. Sometimes the hardest part is beginning. AI can handle the first draft, the initial research, the pre-work, so the person can spend their time reviewing, deciding, and improving the result.
Analysis is where the conversation gets more interesting. AI can work through spreadsheets, financial reports, sales data, surveys, and operational information. But the value comes from asking better questions. Not just: summarize this report. Instead: what changed, what stands out, what are the anomalies, what trends do you see, what am I missing?
That last question can be especially useful. The value is sometimes not in getting an answer. It is in identifying the questions you should be asking about the information you already have.
From a single prompt to a repeatable process.
Many people start with a one-time prompt. You ask AI to do something, you get an answer, you move on. But if you find yourself asking for the same thing again and again, that is your opportunity. A repeatable prompt can become part of a workflow. Eventually that workflow can be automated. That is where AI stops being something you experiment with and starts becoming part of how work gets done.
Look at What You Already Have
Before buying another AI platform, look at the tools you already use. Microsoft 365, Google Workspace, your CRM, your accounting system, your project management software, your marketing platform, your document management system. Many of these already include AI capabilities.
Every new platform creates another login, another vendor, another system to train people on, and potentially another place where your information is stored or processed. In Connecticut, you need to be aware of that because of the Data Privacy Act. Data processing by AI is a trigger under that law. Sometimes the right answer is not adding another tool. It is getting more value out of what you already have.
QuickBooks Online, for example, is in beta with the ability to chat with your own financial data. Adobe Acrobat can summarize long PDFs. These capabilities already exist inside tools many organizations are already paying for.
The Tool Is Only Part of What Produces a Good Result
AI needs context. It needs instructions. It may need examples, company information, access to documents, or feedback on what good looks like. You would not hire someone and give them no training or context and then expect them to understand exactly what you wanted. AI is the same. Better inputs, clearer instructions, and better information will generally produce better results.
Iteration matters too. When a response starts heading in the wrong direction, you can go back to an earlier version and take a different path. Many AI platforms call this branching. You return to the point where things were still working, go a different direction, and keep moving. That is a useful tool when you are working through something complex.
Two things to be aware of as you use AI more broadly. First, platforms like Facebook, LinkedIn, and others now have AI detection. If your public content is generated by AI, it may be suppressed. Second, intellectual property created with AI is not automatically yours. There was a case recently where an author was removed from award consideration because the work appeared to have been generated with AI assistance. These things matter depending on how and where you are using the output.
And if you are giving AI access to client information, donor data, financial records, email, or internal documents, that is an entirely different level of consideration than marketing copy. You need to understand what the tool can see, what it stores, how that information is used, and who has access to it. Security and tool selection have to be the same conversation.
Five Steps to Get Started
Start with one task. Define the outcome you want. Figure out what AI needs to do the job, the same way you would think through a new hire. Choose the simplest tool that fits. Do not chase the most powerful platform when a simpler one does what you need. Protect the information involved. And then measure the result.
Did it save time? Did it improve the quality of the work? Did it remove unnecessary steps? If yes, refine it and apply what you learned. If it was not worth the effort, stop and move on to something else. That is how you build practical AI use in your organization without wasting time chasing things that do not fit.
A Word on Scaffolding
Once you have a handle on individual tasks, the next level is scaffolding. Scaffolding is the basic building blocks and tools you need in place to allow AI to work in more advanced ways.
That means controlling access so AI only touches what it should. Creating repeatable prompts and workflows. Setting up the environment, whether cloud-based or local, to support what you need. And doing the unglamorous foundational work: cleaning your data, labeling data sensitivity, and organizing information in a way that supports more advanced workflows.
I mentioned a story from last week. Someone was in their Tesla while it drove itself, talking to AI about a set of tasks he needed completed. He had tools already set up, permissions already configured, workflows already built. By the time he arrived at the office, the work was done. That is advanced. But the things that made it possible are the same fundamentals we have been talking about throughout this series. Know your outcome. Control the access. Build the repeatable process. Give it what it needs to succeed.
AI can be a genuine partner for your staff, your leadership, and your organization. But it works best when you treat it with the same care and intention you would bring to hiring and onboarding a person. Start small, measure what matters, and build from there.
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